In 1984 Karl Popper wrote a private letter to an inquirer he didn’t know, responding to enclosed interview questions. The response was subsequently published and in it he wrote, among other things, that:

“Every intellectual has a very special responsibility. He has the privilege and opportunity of studying. In return, he owes it to his fellow men (or ‘to society’) to represent the results of his study as simply, clearly and modestly as he can. The worst thing that intellectuals can do — the cardinal sin — is to try to set themselves up as great prophets vis-a-vis their fellow men and to impress them with puzzling philosophies. Anyone who cannot speak simply and clearly should say nothing and continue to work until he can do so.”

Aside from the offensive sexism in referring to intellectuals as males, there is another way this imperative should be updated for intellectualism today. The movement to make data available online is picking up momentum — as it should — and open code is following suit (see http://mloss.org for example). But data should not be confused with facts, and applying the simple communication that Popper refers to beyond the written or spoken word is the only way open data will produce dividends. It isn’t enough to post raw data, or undocumented code. Data and code should be considered part of intellectual communication, and made as simple as possible for “fellow men” to understand. Just as knowledge of adequate English vocabulary is assumed in the nonquantitative communication Popper refers to, certain basic coding and data knowledge can be assumed as well. This means the same thing as it does in the literary case; the elimination of extraneous information and obfuscating terminology. No need to bury interested parties in an Enron-like shower of bits. It also means using a format for digital communication that is conducive to reuse, such as a flat text file or another non-proprietary format, for example pdf files cannot be considered acceptable to either data or code. Facilitating reproducibility must be the gold standard for data and code release.

And who are these “fellow men”?

Well, fellow men and women that is, but back to the issue. Much of the history of scientific communication has dealt with the question of demarcation of the appropriate group to whom the reasoning behind the findings would be communicated, the definition of the scientific community. Clearly, communication of very technical and specialized results to a layman would take intellectuals’ time away from doing what they do best, being intellectual. On the other hand some investment in explanation is essential for establishing a finding as an accepted fact — assuring others that sufficient error has been controlled for and eliminated in the process of scientific discovery. These others ought to be able to verify results, find mistakes, and hopefully build on the results (or the gaps in the theory) and thereby further our understanding. So there is a tradeoff. Hence the establishment of the Royal Society for example as a body with the primary purpose of discussing scientific experiments and results. Couple this with Newton’s surprise, or even irritation, at having to explain results he put forth to the Society in his one and only journal publication in their journal Philosophical Transactions (he called the various clarifications tedious, and sought to withdraw from the Royal Society and subsequently never published another journal paper. See the last chapter of The Access Principle). There is a mini-revolution underfoot that has escaped the spotlight of attention on open data, open code, and open scientific literature. That is, the fact that the intent is to open to the public. Not open to peers, or appropriately vetted scientists, or selected ivory tower mates, but to anyone. Never before has the standard for communication been “everyone,” in fact quite the opposite. Efforts had traditionally been expended narrowing and selecting the community privileged enough to participate in scientific discourse.

So what does public openness mean for science?

Recall the leaked files from the University of East Anglia’s Climatic Research Unit last November. Much of the information revealed concerned scientifically suspect (and ethically dubious) attempts not to reveal data and methods underlying published results. Although that tack seems to have softened now some initial responses defended the climate scientists’ right to be closed with regard to their methods due to the possibility of “denial of service attacks” – the ripping apart of methodology (recall all science is wrong, an asymptotic progression toward to truth at best) not with the intent of finding meaningful errors that halt the acceptance of findings as facts, but merely to tie up the climate scientists so they cannot attend to real research. This is the same tradeoff as described above. An interpretation of this situation cannot be made without the complicating realization that peer review — the review process that vets articles for publication — doesn’t check computational results but largely operates as if the papers are expounding results from the pre-computational scientific age. The outcome, if computational methodologies are able to remain closed from view, is that they are directly vetted nowhere. Hardly an acceptable basis for establishing facts. My own view is that data and code must be communicated publicly with attention paid to Popper’s admonition: as simply and clearly as possible, such that the results can be replicated. Not participating in dialog with those insufficiently knowledgable to engage will become part of our scientific norms, in fact this is enshrined in the structure of our scientific societies of old. Others can take up those ends of the discussion, on blogs, in digital forums. But public openness is important not just because taxpayers have a right to what they paid for (perhaps they do, but this quickly falls apart since not all the public are technically taxpayers and that seems a wholly unjust way of deciding who shall have access to scientific knowledge and who not, clearly we mean society), but because of the increasing inclusiveness of the scientific endeavor. How do we determine who is qualified to find errors in our scientific work? We don’t. Real problems will get noticed regardless of with whom they originate, many eyes making all bugs shallow. And I expect peer review for journal publishing to incorporate computational evaluation as well.

Where does this leave all the open data?

Unused, unless efforts are expended to communicate the meaning of the data, and to maximize the usability of the code. Data is not synonymous with facts – methods for understanding data, and turning its contents into facts, are embedded within the documentation and code. Take for granted that users understand the coding language or basic scientific computing functions, but clearly and modestly explain the novel contributions. Facilitate reproducibility. Without this data may be open, but will remain de facto in the ivory tower.

4 Responses to “Open Data Dead on Arrival”

I really liked how you highlight the difference in scientific audiences. But aren’t data and facts one and the same? What we really need to communicate is information and knowledge.

E.g., there is the data series of tree-ring proxies for temperature. But in itself this doesn’t tell one anything. You need the context in which that data operates: that tree-rings respond thusly to warm periods, that we can use this to reconstruct the temperature back then, that this might indicate a change in climate from then to now …

Great post. I am also a firm believer of releasing data as openly as possible. I recently launched a scientific data sharing website called BioTorrents.net, and would encourage anyone that wants to share their data check out that resource.

Neil, Thanks for the excellent comments. Data is different from facts in that a fact has the additional requirement of social acceptance – scientific facts are constructed through a process by which they become embedded in a body of knowledge we all draw upon. This is quite different from data – a series of raw numbers for example certainly qualifies as data but must go through additional processed to become accepted as fact. I argue these additional processes, often recorded in code, should be a simple to understand as possible and permit the reproducibility of results (the potential facts).

Getting most coders (and those who deal with data) to put things simply is just as difficult as getting businesspeople to speak plainly: “from now on” becomes “on a go-forward basis”, etc.

In coding, we have a UML forest of obfuscation before any actual work can be done, and a lot of the time, it doesn’t help at all.

So what’s getting in the way? It has to be ego. What technical argument is there? Speaking plainly does nothing for anyone’s inflated or delusional self-image, and by the same token, writing plain, readable code is anathema to the coder wanting to be seen as unapproachably brilliant.

Let’s hope a more modest culture can emerge in data management and coding. I’d very much like to see you get your wish, and have the simplest possible approach win.